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Graph/pyspark

Package

pyspark

pyspark

pyspark

Node Details

Public graph metadata

Updated4/30/2026

PageRank0.0000

Connections

6 linked nodes

AFFECTSRootCause

Every file read has overhead - metadata calls, HTTP requests, etc. — tons of small files on S3. Tension: When you multiply that by 100,000 files, it kills performance. Outcome: Turns out Spark really struggles with tons of small files on S3.

AFFECTSRootCause

could not find way how to provide this file pattern in python script. Outcome: Convert DynamicFrame to Pandas DataFrame.

AFFECTSRootCause

Do you really need to import all the data within Glue in order to update the status? — If the logic is simple. Tension: import all the data within Glue in order to update the status. Outcome: I would only schedule a job to run a script like this on my database.

AFFECTSRootCause

DMS creates new table — move data to dynamo db. Tension: i want data in the old table. Outcome: it just allows you to create the script in an abstract way using rules and mappings.

REQUIRESSolution

Use pandas : — Convert DynamicFrame to Pandas DataFrame. Tension: Define the S3 path for the output file. Outcome: Upload the buffer to S3.

REQUIRESSolution

If your job is simple and can be solved using vanilla python, you should choose that. — AWS Glue ETL job to update data stored in S3 Objects to an existing table in DDB. Tension: There is no official numbers provided by AWS. Outcome: Also consider using packages i.e. Pandas, PyArrow etc. for performance improvement.

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